用事件自适应压缩提升多模态时序数据的效率与精度
EvtGraph: Event-Adaptive Compression for Sparse Temporal Graph Learning in Multimodal Time Series
- 按事件密度动态压缩序列，只保留关键时间点
- 在有限计算预算下仍保持高精度，小预算即可有效运行
- 适合临床等冗余度高的时序数据，兼顾性能与效率
多模态时序数据天然具有不规则和信息密度不均的特点，但多数模型依赖均匀离散化，导致表示效率低下。我们提出 extbf{EvtGraph}，一个统一框架，在明确预算约束下使计算与时间显著性对齐。通过事件自适应压缩（EAMC）将序列重参数化为事件级标记，基于节点预算（NBC）选择紧凑子集，并执行时序约束的稀疏图推理（T2SG）。该设计将密集序列转化为对显著事件的结构化计算，降低复杂度的同时保留关键状态转移。实验表明，该方法在多模态临床数据（MIMIC-IV + CXR）及跨领域基准上优于基于Transformer和循环结构的基线模型，显著提升效率。结果表明，受预算约束的事件中心表征是处理高冗余时序数据的一般范式。
原文摘要 · Abstract (English)
Multimodal temporal data are inherently irregular and uneven in information density, yet most models rely on uniform discretization, leading to inefficient representations. We propose \textbf{EvtGraph}, a unified framework that aligns computation with temporal salience under explicit budget constraints. EvtGraph reparameterizes sequences into event-level tokens via event-adaptive compression (EAMC), selects a compact subset with a node budget (NBC), and performs temporally constrained sparse graph reasoning (T2SG). This transforms dense sequences into structured computation over salient events, reducing complexity while preserving critical transitions. We show that this design provides a practical mechanism for allocating representational capacity under a fixed budget, yielding a consistent performance--efficiency trade-off, where a small budget is often sufficient in practice. Experiments on multimodal clinical (MIMIC-IV + CXR) and cross-domain benchmarks demonstrate that EvtGraph outperforms both Transformer-based and recurrent baselines while significantly improving efficiency. These results suggest that budget-constrained event-centric representation provides a general paradigm for learning from high-redundancy temporal data.
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